Abstract
Respiratory Sinus Arrhythmia, the variation in the heart rate synchronized with the breathing cycle, forms an interconnection between cardiac-related and respiratory-related signals. It can be used by itself for diagnostic purposes, or by exploiting the redundancies it creates, for example by extracting respiratory rate from an electrocardiogram (ECG). To perform quantitative analysis and patient specific modeling, however, simultaneous information about ventilation as well as cardiac activity needs to be recorded and analyzed. The recent advent of medically approved Electrical Impedance Tomography (EIT) devices capable of recording up to 50 frames per second facilitates the application of this technology. This paper presents the automated selection of a cardiac-related signal from EIT data and quantitative analysis of this signal. It is demonstrated that beat-to-beat intervals can be extracted with a median absolute error below 20 ms. A comparison between ECG and EIT data shows a variation in peak delay time that requires further analysis. Finally, the known coupling of heart rate variability and tidal volume can be shown and quantified using global impedance as a surrogate for tidal volume.
Highlights
Electrical Impedance Tomography (EIT) is a powerful imaging tool
Beat-to-beat intervals (BBI) were calculated from the arrays containing the peaks. Since these time intervals are by definition unevenly sampled and located on an irregular grid, the beat-to-beat intervals (BBI) derived from cardiac related signal (CRS) were linearly interpolated to the locations of the BBI obtained from ECG
These errors greatly influence root mean square error (RMSE), whereas median absolute error (MAE) is influenced only minimally. This is especially prominent in run 9, where the correlation coefficient is below 0.7 and RM SE is above 90 ms, while the MAE shows a very low value below 10 ms, proving that singular outliers influence the result
Summary
Electrical Impedance Tomography (EIT) is a powerful imaging tool It seeks to reconstruct the impedance distribution inside a patient from measurements on the boundary. These measurements are non-invasive, painless and have no known side effects. Since the electrical impedance of lung tissue varies greatly with air content, the most common use for medical EIT is in pulmonary monitoring. It serves to visualize and analyze the regional distribution of ventilation, which in turn can be used for example to automatically optimize the respirator settings for mechanically ventilated patients [1]. A question still unanswered is the optimal electrode configuration to maximize the quality of the cardiac related signal [2]
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